Transcript Multi-Sensor Precipitation Estimation Presented by D.-J. Seo

Multi-Sensor Precipitation
Estimation
Presented by
D.-J. Seo1
Hydrologic Science and Modeling Branch
Hydrology Laboratory
National Weather Service
Presented at the NWSRFS International
Workshop, Kansas City, MO, Oct 21, 2003
1 [email protected]
In this presentation
• An overview of multisensor precipitation
estimation in NWS
– The Multisensor Precipitation Estimator
(MPE)
• Features
• Algorithms
• Products
– Ongoing improvements
– Summary
DHR
DPA
WSR-88D
ORPG/PPS
Hydro-Estimator
Rain Gauges
Flash Flood Monitoring
and Prediction (FFMP)
Lightning
Multi-Sensor Precipitation
Estimator (MPE)
NWP
model
output
WFO
RFC, WFO
Multi-Sensor Precipitation Estimator
(MPE)
• Replaces Stage II/III
• Based on;
– A decade of operational experience with
NEXRAD and Stage II/III
– New science
– Existing and planned data availability from
NEXRAD to AWIPS and within AWIPS
– ‘Multi-scale’ accuracy requirements (WFO,
RFC, NCEP, external users)
Stage III versus MPE
• No delineation of
effective coverage of
radar
• Radar-by-radar
precipitation analysis
• Mosaicking without
explicit considerations
of radar sampling
geometry
• Delineation of effective
coverage of radar
• Mosaicking based on
radar sampling
geometry
• Precipitation analysis
over the entire service
area
• Improved mean-field
bias correction
• Local bias correction
(new)
Delineation of Effective Coverage of
Radar
• Identifies the areal extent where radar can
‘see’ precipitation consistently
• Based on multi-year climatology of the
Digital Precipitation Array (DPA) product
(hourly, 4x4km2)
• RadClim - software for data processing and
interactive delineation of effective coverage
Radar Rainfall Climatology - KPBZ (Pittsburg, PA)
Warm season
Cool season
Mosaicking of Data from Multiple
Radars
• In areas of coverage overlap, use the radar
rainfall estimate from the lowest
unobstructed1 and uncontaminated2 sampling
volume
1
free of significant beam blockage
2 free of ground clutter (including that due to
anomalous propagation (AP))
Mid-Atlantic River Forecast Center (MARFC)
Height of Lowest Unobstructed Sampling Volume
Radar Coverage Map
West Gulf River Forecast Center (WGRFC)
Height of Lowest Unobstructed Sampling Volume
Radar Coverage Map
Southeast River Forecast Center (SERFC)
Height of Lowest Unobstructed Sampling Volume
Radar Coverage Map
PRECIPITATION MOSAIC
RADAR COVERAGE MAP
Mean-Field Bias (MFB) Correction
• Based on (near) real-time hourly rain gauge
data
• Equivalent to adjusting the multiplicative
constant in the Z-R relationship for each radar;
Z = A(t) Rb
• Accounts for lack of radar hardware calibration
• Designed to work under varying conditions of
rain gauge network density and posting delays
in rain gauge data
• For details, see Seo et al. (1999)
From Cedrone 2002
MFB and Z-R List
North-Central River Forecast Center
(NCRFC)
Effect of Mean Field Bias Correction
From Seo et al. 1999
Local Bias (LB) Correction
• Bin-by-bin (4x4km2) application of mean
field bias correction
• Reduces systematic errors over smaller
areas
• Equivalent to changing the multiplicative
constant in the Z-R relationship at every bin
in real time; Z = A(x,y,t) Rb
• More effective in gauge-rich areas
• For details, see Seo and Breidenbach (2000)
Local Bias
Radar underestimation
(local bias > 1)
Radar overestimation
(local bias < 1)
Local Bias Adjustment
Local biascorrected
rainfall =
local bias x
raw radar
rainfall
Multi-Sensor Analysis
• Objective merging of rain gauge and biascorrected radar data via optimal estimation
(Seo 1996)
• Reduces small scale errors
• Accounts for spatial variability in precipitation
climatology via the PRISM data (Daly 1996)
Multi-Sensor Analysis
1) Start with 1 hour radar
accumulations (HDP) which may
contain mean and local biases
B
A
2) Remove mean field bias
BIAS
A
R = Bias*R
B
Cross Section
3) Merge Gage and Radar Observations
Re=w1G1 + w2G2 +w3G3+w4R
A
B
Cross Section
MULTISENSOR ANALYSIS
ALSO FILLS MISSING AREAS
Multisensor analysis accounts for spatial
PRISM Climatology
variability inJune
precipitation
climatology
July PRISM climatology
MPE products
• All products are hourly and on the HRAP grid
(4x4km2)
• RMOSAIC - mosaic of raw radar rainfall
• BMOSAIC - mosaic of mean field biasadjusted radar rainfall
• GMOSAIC - gauge-only analysis
• MMOSAIC - multi-sensor analysis of
BMOSAIC and rain gauge data
• LMOSAIC - local bias-adjusted RMOSAIC
Human Input via Graphical User
Interface
• Through HMAP-MPE (a part of HydroView)
• Allows interactive
– quality control of raw data, analysis, and
products
– adjustment, draw-in and deletion of
precipitation amounts and areas
– manual reruns (i.e. reanalysis)
• For details on HMAP-MPE, see Lawrence et
al. (2003)
Ongoing improvements
• Quality-control of rain gauge data
(Kondragunta 2002)
– automation
– multisensor-based
• local bias correction of satellite-derived
precipitation estimates1 (Kondragunta et al.
2003)
• Objective integration of bias-corrected
satellite-derived estimates into multisensor
analysis
1
Hydro-estimator (formerly Auto-estimator) product
from NESDIS (Vicente et al. 1998)
Satellite Precip Estimate
Satellitederived
estimates
fill in radar
data-void
areas
West Gulf River
Forecast Center
(WGRFC)
From Kondragunta 2002
After Bias
Correction 
Merging
radar, rain
gauge,
satellite
and
lightning
data
From Kondragunta 2002
Summary
• Multisensor estimation is essential to quantitative use
of remotely sensed precipitation estimates in
hydrological applications
• Built on the experience with NEXRAD and Stage II/III
and new science, the Multisensor Precipitation
Estimator (MPE) offers an integrated and versatile
platform and a robust scientific algorithm suite for
multisensor precipitation estimation using radar, rain
gauge and satellite data
• Ongoing improvements includes multisensor-based
quality control of rain gauge data and objective
merging of satellite-derived precipitation estimates
with radar and rain gauge data
Thank you!
For more information, see
http://www.nws.noaa.gov/oh/hrl/papers/
papers.htm